Implementasi Algoritma Haar Cascade Classifier Dalam Mendeteksi Robot Sepak Bola Beroda

Authors

  • Joshua Sitompul Universitas Dinamika Bangsa, Jambi
  • M Irwan Bustami Universitas Dinamika Bangsa, Jambi
  • Desi Kisbianty Universitas Dinamika Bangsa, Jambi

DOI:

https://doi.org/10.30865/mib.v6i4.3929

Keywords:

OpenCV, Python, Object Detection, Haar Cascade Classifier, Computer Vision.

Abstract

In soccer robot contests, generally soccer robots can recognize their own team robots through color detection using the HSV model. Robots that use color detection to identify their own team robot can detect objects that have the same color value, so objects that have the same color will be considered as their own team robot. However, if you only rely on this method, it is still lacking when viewed in terms of object tracking. Haar like feature or known as Haar Cascade Classifier is a rectangular (square) feature method, which gives a specific indication of an image. This method is able to detect quickly and in real time. Therefore, the researchers tried to unite these two detections to be able to produce a better detection system than before. It is hoped that hoped that in this study, researchers can provide input for the Robotics team of Dinamika Bangsa University in improving the accuracy of robot detection against robots in the same team. From the results of the study it was found that the detection of objects with a distance from the camera as far as 100 cm to 150 cm was successfully carried out, but with a distance of more than 150 cm the object was not detected.

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Published

2022-10-25

Issue

Vol. 6 No. 4 (2022): Oktober 2022

Section

Articles

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